With the advent of an advanced information society, computers have come to handle a vast amount of data, and there have been an increasing number of cases where a large number of computers are installed and managed collectively in the same room of a facility such as a data center. For example, a large number of racks (server racks) are installed in a computer room of a data center, and multiple computers (servers) are housed in each rack. A huge amount of jobs are processed efficiently by organically allocating the jobs to the computers according to the operating condition of each computer.
Computers generate a lot of heat during operation. Since temperature increase inside a computer may cause malfunction or failure of the computer, the heat generated inside the computer is exhausted to the outside of a rack by taking in cool air inside the rack using a cooling fan. In order to adjust the temperature of a heat generator (such as a CPU) of the computer to be equal to or lower than a target value, the number of rotations of the cooling fan is feedback controlled by proportional-integral-derivative (PID) control, for example.
Meanwhile, model predictive control is a control method to optimize a manipulated variable in a future interval. The model predictive control is a method of predicting a variation of a controlled variable (a CPU temperature, for example) in a predetermined prediction interval by use of a prediction model, and determining a manipulated variable so that the controlled variable may reach its target value in a desirable way. A manipulated variable is determined by evaluating a manipulated variable for each control cycle based on an evaluation function, and obtaining a manipulated variable with the highest evaluation value.
A prediction model is a model replicating the dynamic characteristics of an object to be controlled. The dynamic characteristics indicate a relationship of time series variation between a manipulated variable input into the object to be controlled and a controlled variable output from the object to be controlled. In the model predictive control, the accuracy in the reproducibility of the dynamic characteristics greatly influences the control performance. There is also proposed a solution using a nonlinear model as a prediction model used in model predictive control. However, the use of a nonlinear model includes problems such that a solution for an optimum manipulated variable is not given or calculation processing is not completed within a control time period when the prediction model is huge or complicated. Thus, a linear prediction model is generally used, and a solving method for model predictive control with a linear model has been established already.
The above-described technique is disclosed, for example, in Japanese Laid-open Patent Publication No. 2012-251770.